Visual sign language translation training device and method

ABSTRACT

Methods, devices and systems for training a pattern recognition system are described. In one example, a method for training a sign language translation system includes generating a three-dimensional (3D) scene that includes a 3D model simulating a gesture that represents a letter, a word, or a phrase in a sign language. The method includes obtaining a value indicative of a total number of training images to be generated, using the value indicative of the total number of training images to determine a plurality of variations of the 3D scene for generating of the training images, applying each of plurality of variations to the 3D scene to produce a plurality of modified 3D scenes, and capturing an image of each of the plurality of modified 3D scenes to form the training images for a neural network of the sign language translation system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent document is a continuation of U.S. patent application Ser.No. 16/258,531, entitled “VISUAL SIGN LANGUAGE TRANSLATION TRAININGDEVICE AND METHOD” and filed on Jan. 25, 2019, which claims the benefitsand priority of U.S. Provisional Patent Application No. 62/654,174entitled “OPTIMIZING TRAINING FOR VISUAL SIGN LANGUAGE,” filed Apr. 6,2018 and U.S. Provisional Patent Application No. 62/629,398 entitled“INTERACTIVE AUTOMATED SIGN LANGUAGE TRANSLATION METHOD AND APPARATUS,”filed Feb. 12, 2018. The entire contents of the before-mentioned patentapplications are incorporated by reference as part of the disclosure ofthis patent document.

TECHNICAL FIELD

This document generally relates to automated pattern and gesturerecognition, and more particularly to improving training of automatedpattern and gesture recognition systems that utilize neural networks.

BACKGROUND

Computer vision is an interdisciplinary field that deals with howcomputers can gain high-level understanding from digital images orvideos. Computer vision tasks include methods for acquiring, processing,analyzing and understanding digital images, and extraction ofhigh-dimensional data from the real world in order to produce numericalor symbolic information. From the perspective of engineering, computervision techniques seek to automate tasks that the human visual systemcan do, such as pattern recognition for recognizing patterns andregularities in data, and gesture recognition for interpreting humangestures via mathematical algorithms.

One specification application of automated pattern and gesturerecognition is sign language translation. A sign language (also known asa signed language) is a language that uses manual communication toconvey meaning, ideas and thoughts, which simultaneously employs handgestures, movement, orientation of the fingers, arms or body, and facialexpressions to convey a speaker's ideas. Pattern and gesture recognitiontechniques can facilitate the automated translation of sign languages.

SUMMARY

Disclosed are devices, systems and methods for improving the training ofautomated pattern and gesture recognition systems. The disclosedtechniques can be applied in various embodiments, such as interactiveautomated sign language translation and communication, to improve theperformance and accuracy of a recognition system and allow the system torecognize a larger number of characteristics more accurately and moreefficiently.

One aspect of the disclosed technology relates to an apparatus fortraining a sign language translation system that a processor and amemory including processor executable code. The processor executablecode, upon execution by the processor, causes the processor to generatea three-dimensional (3D) scene that includes a 3D model representing atleast a part of a human body. The 3D model is positioned in the 3D sceneto simulate a gesture that represents a letter, a word, or a phrase in asign language. The processor executable code, upon execution by theprocessor, also configures the processor to obtain a value indicative ofa total number of training images to be generated, use the valueindicative of the total number of training images to determine aplurality of variations of the 3D scene for generating of the trainingimages, apply each of plurality of variations to the 3D scene to producea plurality of modified 3D scenes, and capture an image of each of theplurality of modified 3D scenes to form the training images for a neuralnetwork of the sign language translation system.

Another aspect of the disclosed technology relates to a method forproviding training images for training a neural network of a signlanguage translation system. The method includes generating athree-dimensional (3D) scene that includes a 3D model representing atleast a part of a human body. The 3D model is positioned in the 3D sceneto simulate a gesture that represents a letter, a word, or a phrase in asign language. The method also includes obtaining a value indicative ofa total number of training images to be generated, using the valueindicative of the total number of training images to determine aplurality of variations of the 3D scene for generating of the trainingimages, applying each of plurality of variations to the 3D scene toproduce a plurality of modified 3D scenes, and capturing an image ofeach of the plurality of modified 3D scenes to form the training imagesfor a neural network of the sign language translation system.

Another aspect of the disclosed technology relates to a non-transitorycomputer readable medium having code stored thereon. The code, uponexecution by a processor, causes the processor to implement a methodthat includes generating a three-dimensional (3D) scene that includes a3D model representing at least a part of a human body, wherein the 3Dmodel positioned in the 3D scene to simulate a gesture that represents aletter, a word, or a phrase in a sign language, obtaining a valueindicative of a total number of training images to be generated, usingthe value indicative of the total number of training images to determinea plurality of variations of the 3D scene for generating of the trainingimages, applying each of plurality of variations to the 3D scene toproduce a plurality of modified 3D scenes, and capturing an image ofeach of the plurality of modified 3D scenes to form the training imagesfor a neural network of the sign language translation system.

Yet another aspect of the disclosed technology relates to an apparatusfor training a pattern recognition system having a neural networkengine. The apparatus includes one or more processors and a memoryincluding processor executable code. The processor executable code, uponexecution by the one or more processors, causes the one or moreprocessors to generate a three-dimensional (3D) scene that includes a 3Dmodel representing an object. The 3D model includes a plurality ofpolygonal subsections that collectively form the object. The processorexecutable code, upon execution by the one or more processors, alsocauses the one or more processors to determine a total number oftraining images to be generated for training the neural network,determine, based on the total number of training images, a plurality ofparameter variations, and applying each of plurality of the parametervariations to the 3D scene to produce a plurality of modified 3D scenes.The modified 3D scenes include at least one set of variations to aspatial position of the moving object in accordance with a temporalsequence. The processor executable code, upon execution by the one ormore processors, also causes the one or more processors to capture animage of each of the plurality of modified 3D scenes to form thetraining images for the neural network learning engine, and, for each ofthe training images, automatically generate a label that corresponds toa feature of interest of the 3D model. The label includes one or morebounding lines that delineates a precise boundary of the feature ofinterest by combining an integer number of polygonal subsections of the3D model.

These and other features of the disclosed technology are described inthe present document.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a two-way translation system used by two parties inaccordance with an example embodiment of the disclosed technology.

FIG. 2 illustrates a remote two-way translation system used by twoparties that may be in different locations over a communication networkin accordance with an example embodiment of the disclosed technology.

FIG. 3 illustrates a one-way translation system used by two parties inaccordance with an example embodiment of the disclosed technology.

FIG. 4 illustrates another two-way interactive translation systemimplemented to enable communications by two parties in accordance withan example embodiment of the disclosed technology.

FIG. 5 illustrates a configurable automated translation system inaccordance with an example embodiment of the disclosed technology.

FIG. 6 illustrates another configurable automated translation system inaccordance with an example embodiment of the disclosed technology.

FIG. 7 illustrates yet another configurable automated translation systemin accordance with an example embodiment of the disclosed technology.

FIG. 8A illustrates one view of an image capture and processing devicethat can be used for automated sign language translation in accordancewith an example embodiment of the disclosed technology.

FIG. 8B shows another view of an image capture and processing devicethat can be used for automated sign language translation in accordancewith an example embodiment of the disclosed technology.

FIG. 9 illustrates a flow diagram of operations that can be carried outby various component to implement automated sign language translation inaccordance with an example embodiment of the disclosed technology.

FIG. 10 illustrates a method that includes a set of operations that canbe carried out to automate sign language translation in accordance withan example embodiment of the disclosed technology.

FIG. 11 illustrates an amount of noise and/or unwanted features that canbe introduced using regular-shaped labels in pattern recognition.

FIG. 12 illustrates three aspects that optimization can be performed toimprove pattern and gesture recognition systems in accordance with anexample embodiment of the disclosed technology.

FIG. 13 illustrates representative operations taken by a training systemimplemented in accordance with an example embodiment the disclosedtechnology.

FIG. 14A illustrates a rendering of a monkey head and two hands inaccordance with an example embodiment of the disclosed technology.

FIG. 14B illustrates another rendering of the monkey head and the twohands in accordance with an example embodiment of the disclosedtechnology.

FIG. 14C illustrates an example hierarchy of polygons in athree-dimensional (3D) model in accordance with an example embodiment ofthe disclosed technology.

FIG. 15A illustrates a set of operations that are carried out togenerate training images for a letter of the alphabet in accordance withan example embodiment of the disclosed technology.

FIG. 15B depicts a high-level overview of 3D model generation work flowin accordance with an example embodiment of the disclosed technology.

FIG. 16 illustrates a flowchart of an example method for training a signlanguage translation system in accordance with an example embodiment ofthe disclosed technology.

FIG. 17 illustrates a flowchart of another example method for training asign language translation system in accordance with an exampleembodiment of the disclosed technology.

FIG. 18 is a block diagram illustrating an example of the architecturefor a computer system or other control device that can be utilized toimplement various portions of the presently disclosed technology.

DETAILED DESCRIPTION

Pattern recognition is the automated recognition of patterns andregularities in data. Gesture recognition focuses on a specific type ofpattern: gestures, which can originate from any bodily motion or state.Pattern and gesture recognitions are closely related to artificialintelligence and machine learning. In machine learning, pattern andgesture recognition is accomplished by assigning labels to images, ormore generally, to inputs, which allows the input to be recognizedthrough the use of artificial intelligence systems. In many cases,pattern and gesture recognition systems are trained from labeledtraining data using one or more training methods. Among the manyalgorithms that can be implemented to perform the training, such asdecision trees, Bayesian classifiers, and linear/quadratic discriminantanalysis, the use of neural networks is gaining an increasing amount ofattention in the field of artificial intelligence. A neural network, inthe case of machine learning, is an interconnected group of artificialneurons that uses a mathematical or computational model for informationprocessing based on connectionism.

One specific application of using the neural networks for pattern andgesture recognition is sign language translation. Sign languages areextremely complex. In general, sign languages do not have any linguisticrelation to the spoken languages of the lands in which they arise. Thecorrelation between sign and spoken languages is complex and variesdepending on the country more than the spoken language. For example, theUS, Canada, UK, Australia and New Zealand all have English as theirdominant language, but American Sign Language (ASL), used in the US andEnglish-speaking Canada, is derived from French Sign Language whereasthe other three countries sign dialects of British, Australian, and NewZealand Sign Language (collectively referred to as BANZSL). Similarly,the sign languages of Spain and Mexico are very different, despiteSpanish being the national language in each country.

Furthermore, unlike spoken languages, in which grammar is expressedthrough sound-based signifiers for tense, aspect, mood, and syntax, signlanguages use hand movements, sign order, and body and facial cues tocreate grammar. In some cases, even certain uttered sounds or clicks mayform a part of the sign language. Such a cue is referred to as anon-manual activity and can vary significantly across different signlanguages. It is desirable for a sign-language translation system tocapture and process both the hand movements and the non-manualactivities to provide an accurate and natural translation for theparties.

Embodiments of the disclosed technology that are implemented for signlanguage translation are flexible and adaptable in that an input signlanguage, which can be any one of a several sign languages, is convertedto an internal representation, which can then be used to translate theinput sign language into one or more of a variety of output signlanguages. Furthermore, the embodiments described in this documentemploy a multiplicity of different sensors and processing mechanisms tobe able to capture and process information that may not be obtainablewhen a single sensor or process is utilized, and to facilitate accuratecapture, processing and interpretation of the information to allowtranslation between different sign languages. In an example, the Biblemay be translated from any language to a particular sign language, orfrom one sign language representation to another, based on theembodiments disclosed in this document. In general, any textual, audibleor sign language content may be translated in real-time to correspondingcontent in another audible, textual or sign language.

FIGS. 1-10 are illustrations offered to provide the proper context forthe specific application of a sign language translation system that canbenefit from the training techniques described in later sections of thisdocument. FIG. 1 illustrates a two-way translation system used by twoparties in accordance with an example embodiment of the disclosedtechnology. As shown in FIG. 1, a device 110 facilitates communicationbetween a first party 101 and a second party 102. The device 110comprises two sets of sensor inputs and outputs for each of the users.In an example, an outgoing communication of the first party (who may bea sign language user) may be a visual language, a facial expression, ora textual language or input. The device 110 identifies the language usedby the first party and translates it into a language understandable bythe second party, and outputs it based on a preference of the secondparty. In another example, as a part of the incoming communication, thedevice may provide the translated output as a visual language (e.g.another sign language) that may include glyphs, animations or videosynthesis (e.g. avatars), or in an audible or textual language.

This process can be inverted by the device in that an outgoingcommunication of the second party, which now may also be in an audiblelanguage, is identified and translated for the first party. The devicemay output the translation as an incoming communication for the party asa type of visual language or a textual language. The device may inputthe visual language, audible language, facial expression, or texturallanguage or input as an outgoing communication from the party. In someembodiments, the language choice or preference of either party may beidentified by the device. In other embodiments, the language choice orpreference may be predetermined or selected in real-time. It is notedthat the example system of FIG. 1 allows communications between two signlanguage users, or a sign language user and a non-sign language user.

FIG. 2 illustrates a remote two-way translation system used by twoparties that may be in different locations over a communication networkin accordance with an example embodiment of the disclosed technology. Asshown in FIG. 2, the first party 201 and a second party 202 need notnecessarily be co-located as long as they have access to a communicationnetwork that allows the exchange of information from one location toanother location. In the depicted scenario, two devices 210 and 220 areconnected via a communication network, which can be a wired network or awireless network such as a Wi-Fi network, a personal area network, or amobile network. As in the case of FIG. 1, the remote two-way translationsystem allows communications between two sign language users, or a signlanguage user and a non-sign language user.

FIG. 3 illustrates a one-way translation system used by two parties 301,302 in accordance with an example embodiment of the disclosedtechnology. This example includes some features and/or components thatare similar to those shown in FIGS. 1-2, and described above, and theirdescription is not repeated. As shown in FIG. 3, one or more sensors 310capture one or more aspects of the sign language speaker and/or thespeaker's environment and generate a digital representation of what isbeing observed. As will be described in later sections of this document,the one or more sensors 310 can include a variety of audio, video,motion, haptic and other types of sensors. In some embodiments, thevideo rate of the sensor data capture may be selected based on the signlanguage input due to the increased complexity of some sign languages.The digital representation of the sign language communication mayinclude one or more gestures, facial cues, body cues, or environmentalfactors.

The captured information, including the captured video, is thenprocessed by one or more processors 320 to identify the input signlanguage, recognize individual gestures and other features of thecommunication, and translate the communication to an internalrepresentation. The internal representation of the sign languagecommunication can then be converted to an appropriate language and/orformat and displayed or audibly output in the language of the secondparty by various output devices 330, such as displays, speakers, andhaptic devices. In some embodiments, the second language may be either apredetermined language or selected by the second party. In otherembodiments, a second translation or transformation may be performed ifit is detected that certain output devices are not present, or if theuser selects an alternate output option.

FIG. 4 illustrates another two-way interactive translation systemimplemented to enable communications by two parties 401, 402 inaccordance with an example embodiment of the disclosed technology. Asshown in FIG. 4, the translation system includes one or more sensors410, one or more processors 420, and various output devices 430 that aresimilar to the components described above, and their description is notrepeated. In FIG. 4, the one or more sensors 410 are able to receiveaudible or physical input from the second party 402, who wishes tocommunicate with the sign language speaker (the first party 401). Insome embodiments, the translation system includes additional inputinterfaces, such as a keyboard or a touchscreen, to receive physicalinput from the second party 402.

The audible or textual input from the second part is processed by theprocessor and converted to the internal representation. This internalrepresentation of the second party's communication is then translated tothe sign language of the first party 401 and displayed via a secondarydisplay 460. In some embodiments, the first party may receive the inputas text, graphic (glyph-like) or through an animated figurerepresentation of the second party. In other embodiments, the two-waytranslation between a sign language and a textual, audible or differentsign language may be performed in real-time.

FIG. 5 illustrates a configurable automated translation system inaccordance with an example embodiment of the disclosed technology. Asshown in FIG. 5, embodiments of the disclosed technology may include anumber of different visual language sensors 510. In an example, thevisual language sensors may include one or more of an RGB color camera,a monochrome camera, a 3D stereo camera, structured light emitter, a 3Dprocessor of structured light, a time-of-flight emitter and camera, anon-visual electromagnetic sensor and a non-visual electro-opticalsensor. The system may also include standard input devices 515, e.g. amicrophone, a microphone array or 3D microphone, a touchscreen keyboard,or a physical keyboard.

In addition to the input sensors described above, the device includes ahost of output capabilities. For example, standard language renderingmay be performed using a textual display or a speaker 530. On the otherhand, the sign language output may include textual, graphical (glyphs,etc.), animated (virtual hands, avatars, etc.) or synthesized video(from a library of basic visual language gestures) outputs, which can bedemonstrated to the user via another textural display or speaker 540.

FIG. 5 also illustrates that the processing of the input language fromthe first party, and specifically the translation from an input languageto the internal representation and subsequently to the language of thesecond party, can be performed either locally, remotely or both. In someembodiments, the device may have access to cloud computing resources,which may be leveraged in, for example, configurations where manydifferent output sign languages are to be supported.

FIG. 6 illustrates another configurable automated translation system inaccordance with an example embodiment of the disclosed technology. Asshown in FIG. 6, the translation system includes one or more sensors610, one or more processors 620, and various output devices that aresimilar to the components described in the examples above, and thecorresponding description is not repeated. In some embodiments, thefirst party 601 or the second party 602 is not necessarily a person butcould be automata. For example, a sign language user may communicatewith a virtual assistant, an interactive response agent, or simply analert generation mechanism. Embodiments of the disclosed technology areflexible and adaptable to be able to support the translation oflanguages between sign language users, audible language speakers, andautomata, and any combination of the above. In part, this is achieved bytranslating the input language to an internal representation, and thentranslating it to the required one or more output languages.

In an example, the Bible may be translated into American Sign Language(ASL) which is one of the most commonly used sign languages. Expertinput, e.g. interpretation and context for specific verses or sections,may be used to improve the translation during the training period. TheASL-translated Bible may be then displayed using an avatar in a lesscommonly used sign language that is not ASL. In some embodiments, boththe first and second parties may be sign language users, andfurthermore, may not use the same sign language.

FIG. 7 illustrates yet another configurable automated translation systemin accordance with an example embodiment of the disclosed technology.The automated sign language translation system can be used to translatespecific literature or material, e.g. the Bible or works by a particularauthor. In these scenarios, a remote expert 701 may provide additionalcontext and insight as part of the automated translation process. Forexample, idiomatic and situational context related to specific contentmay be used in the training of the neural network and may result in amore natural and useful translation into one of many sign languages.

FIG. 7 illustrates, in part, the digitization of signing activity thatis received using a number of sensors 710 that can sense signingactivities of a user who uses sign language(s) (also referred to as anSL user 702). The captured data is then fed to one or more processors720 for processing. Due to the complexity of sign language, and in aneffort to support many sign languages, the amount of data that iscaptured may be prohibitive. Thus, embodiments of the disclosedtechnology may leverage data that has previously been captured anddigitized to reduce the amount of data that needs to be stored when thedevice is being used in real-time, either locally or in a remotesetting. The device then outputs textual or avatar rendering ofcommunication or content to the SL user via the front display 730 of thedevice.

The device can also include a rear display 740 to show textual or audiocommunication or content to a user that does not use sign languages(also referred to as a non-SL user 703). The device can receive standardaudio or textual communication from the non-SL user and may include arear control 750 for the non-SL user 703 to control the device.

In some embodiments, the device may be effectively used to perform signlanguage translations in a remote region, where access to studios and/ormore sophisticated computer technology is non-existent or very limited.In an example, a basic corpus of a sign language that is used in aremote area may be used to initially train the neural network and willallow translations upon arrival to that region. After the system isdeployed there, the corpus may be expanded exponentially based on inputby native sign language users, which will improve the translationcapabilities due to iterative training and interpretation (or execution)cycles of the neural network.

FIGS. 8A and 8B illustrate different views of an image capture andprocessing device that can be used for automated sign languagetranslation in accordance with an example embodiment of the disclosedtechnology. As shown in FIG. 8A, the image capture and processing devicemay include a right camera 810 and a left camera 850 to be able tocapture a moving object or scene (e.g., a sign language speaker) fromdifferent points of view, therein increasing the depth of fieldmeasurements that enable more accurate interpretation of the scene suchas the sign language gestures. Similarly, the inclusion of a rightmicrophone 820 and a left microphone 840 enable different contextual andenvironmental cues to be captured.

The image capture and processing device further comprises stereo (or 3D)camera 830, a front display 860, and one or more processors 870. In someembodiments, the one or more processors include an ARM Cortext-M3processor and at least one graphics processing unit (GPU). In otherembodiments, and as shown in FIG. 8B, the device may further comprise arear display 880, which may be a touchscreen display. In someembodiments, the stereo camera 830 may be replaced or augmented by adepth sensor or multi-aperture camera, which may be configured tomeasure the “depth” or distance from the camera focal baseline to theobject corresponding to a particular pixel in the scene.

FIG. 9 shows an example flow diagram of operations that can be carriedout by various component to implement automated sign languagetranslation in accordance with one or more embodiments of the disclosedtechnology. This example includes some features and components that aresimilar to those described above, and their description is not repeated.

As shown in FIG. 9, multiple sensors 910 may each capture acommunication of a sign language user. In an example, using multiplesensors enables environmental factors to be acquired, and providesbetter depth of field measurements of sign language gestures. In someexemplary operations, a set of preprocessing operations can beperformed. For example, the input data collected from the multiplesensors is first aligned, in operation 920, both spatially andtemporally. For example, based on the video quality and the externallighting and other conditions, video conditioning procedures 930 (e.g.color space conversion) may be implemented. This operation may befollowed, for example, by spatial and temporal filtering in operation940, to reduce the data to a particular resolution, retain data for onlya particular spatial zone of interest or a temporal period of interest.The processing may further include the application of image and/or videoprocessing methods, e.g. edge detection, which conditions the data foradditional processing.

The conditioned data of the communication from the sign language usercan then be processed in operation 950 in order to extract features ofgestures, facial cues and body cues, amongst other features that enablethe identification of the sign language. The input sign language istranslated to an internal representation in operation 960, andsubsequently translated to the target language in operation 970. Theoutput is then rendered to the user at operation 975.

In some embodiments, the feature extraction, identification andtranslation may be part of a neural network execution process 980.Before the neural network starts the execution process, the neuralnetwork is trained by the neural network learning process 990. Thetechniques discussed in later sections of this document can beimplemented in the neural network learning process to allow the trainedneural network to recognize a large number of characteristics in theinput data more efficiency and more accurately. To perform the neuralnetwork learning process, a set of training data can be used to carryout training algorithms such as supervised training of the neuralnetwork. In some embodiments, as part of feedback for the learningprocess, the translated sign language is used to further train andmodify the neural network, in operation 995, to improve itsidentification and translation capabilities. In yet other embodiments,reinforcement training 998 of neural networks may be employed to improveperformance and increase the flexibility and adaptability of embodimentsof the disclosed technology.

FIG. 10 illustrates a method 1000 that includes a set of operations thatcan be carried out to automate sign language translation in accordancewith an example embodiment of the disclosed technology. The method 1000includes, at operation 1002, receiving a digital representation of acommunication by a user in a first sign language. In some embodiments,the digital representation includes a plurality of images. In otherembodiments, the digital representation includes a video recording.

The method 1000 includes, at operation 1004, identifying the first signlanguage based on at least the set of gestures. In some embodiments,identifying the first sign language may be based on a sign languagegesture library or sign language content curated by an expert. In anexample, the expert content may comprise idiomatic and situationalcontext associated with the first sign language.

The method 1000 includes, at operation 1006, translating thecommunication in the first sign language, based on the identificationand the digital representation, to an internal representation. Themethod 1000 includes, at step 1008, translating the internalrepresentation to at least one of a plurality of sign languagesdifferent from the first sign language. In some embodiments, thetranslation may be based on sign language content curated by an expert.For example, and when translating known subject matter (e.g. the Bible)the expert content may be based on existing interpretation and analysis.

In some embodiments, the method may further include receiving a responseto the communication, which is translated into the internalrepresentation, and subsequently into the first sign language.Embodiments of the disclosed technology are capable of real-timeoperation, which is enabled, in part, by the internal representation andthe underlying neural network.

As noted earlier, the example configurations in FIGS. 1-10 representexamples of systems that capture a variety of information (e.g., video,audio, still images, etc.) in different modalities (e.g., natural light,structured light, infrared light) of moving and still objects, as wellas of the background environment. As a result, a large amount of data isobtained to undergo further processing and analysis to extract theinformation of interest. Generation and analysis of large amounts ofdata are hallmarks of other systems and applications, such as autonomousvehicles and medical applications that involve analysis of medicalimages (e.g., MRI, X-ray, CT scan, video content, etc.). Additionalapplications include, but are not limited to, interactive video games,airport security and surveillance applications, analysis and trainingfor various sports, interactive home devices, and others.

In the above applications, including translations between different signlanguages, the processing capabilities of the described embodimentsinclude the ability to observe and leverage what has been learnt fromeach party in order to provide a desired outcome or result, such asproviding a more natural translation of the communication between thetwo parties. As discussed above, the processing and analysis of theinformation, such as processing and interaction between the parties in asign language translation application, can be implemented using alearning process as part of an artificial intelligence (AI) system suchas a neural network system, to improve the accuracy and the performanceof analysis.

Referring back to FIG. 9, the neural network engine can operate in twomodes: training mode (e.g., the neural network learning process) andinterpretation mode (e.g., the neural network execution process). In thetraining mode, the neural network, which forms a part of the artificialintelligence (AI) core, receives known inputs and associated meaningsand other information. In the interpretation mode, the neural networkengine attempts to identify and interpret the input data that iscollected by, for example, the disclosed interactive sign languagesystem, which also forms part of the learning process. The modes ofoperation can be selectively enabled or disabled to allow the system tobe configured for one or the other mode of operation.

In some embodiments, the training and interpretation of the neuralnetwork may use supervised learning, unsupervised learning, orreinforcement learning techniques at various stages depending on thedata available and the particular learning task being optimized. Theselearning paradigms can be augmented by content by additionalinformation, such as information from sign language experts in signlanguage translation application, which provides situational context andresults in a more natural translation.

In the sections that follow, examples from the sign language translationsystem are used to further illustrate the disclosed enhancements intraining and utilization of neural networks associated with an AIsystem. Training of the translation system can face several challenges.Typically, a large set of training data (e.g., training images) isneeded to allow the system to accurately recognize the target image ofinterest from subsequently collected data, and to, for example, provideaccurate translations for a sign language. It is also desirable for thetraining data to have a certain degree of variance to reduce translationerrors. For example, to allow the system to accurately recognize thehand movement that represents the word “apple” in the ASL, a data set of150 to 300 images in different angles of view are typically needed.Obtaining the data set can be a time-consuming task. Furthermore, it canbe very difficult to obtain a good set of training data for lowresolution sign languages that are not widely used, and thus no readilyavailable data can be obtained. Currently, there is a major lack ofvideo clip or image libraries to furnish a variety of different datacovering distances, angles, and other characteristics required to havegood training media that will result in high accuracy recognitionresults. Even if a person locates or takes enough videos, the contentacross many of the clips overlap, and thus does not add extra value,

In the training mode, the neural network engine also needs theassociated meanings for the training data. Currently, the training datais labeled manually by a system operator or an expert to identity thefeatures of interest in a training image, such as a person's finger,hand, and/or face. The labeling process, however, can be extremelytime-consuming. Referring back to the example of an “apple” in the ASL,after obtaining a training data set of 150 to 300 images, the systemoperator or the expert must manually label the gesture for an apple,i.e., placing the knuckle of the right index finger against the cheek inall images, which is extremely time consuming. The task of manuallabeling may become more cumbersome, or even impossible, whenirregularly-shaped features must be labeled. For example, the systemoperator may create a circular label for the knuckle of the right indexfinger. Because the knuckle has a non-circular shape, the labeled areaincludes noise or unwanted features, which lower the training efficiencyand impact the execution accuracy of the trained translation system.

FIG. 11 illustrates an amount of noise and/or unwanted features that canbe introduced using regular-shaped labels in pattern recognition. InFIG. 11, a system operator labels each person with a rectangular label.Each rectangular label 701, 702 for a person includes a large amount ofbackground pixels 703 (e.g., grass on the playing field), which areconsidered noise and can negatively impact the training efficiency ofthe system.

The disclosed techniques that are described in the following sectionscan be used in various embodiments to efficiently train a neural networkof an AI system, such as a sign-language translation system, tofacilitate identification, understanding and translation of one or morefeatures of interest using a small set of training data. The disclosedtechniques can be implemented in various embodiments to significantlysurpass the efficiency and capabilities of the existing trainingprocesses. As illustrated in FIG. 12, the disclosed optimizationtechniques can be applied to one or all of the following aspects toimprove the training of a pattern and gesture recognition system:performance 1201 (e.g., the execution time of a recognition event), thenumber of classes/characteristics 1202 (e.g., the number of differentimages, or regions of an image, that can be recognized), and accuracy1203 (e.g., the percentage that a recognition event properly identifiesthe input pixels).

Section headings below are used only to improve readability and do notlimit scope of the disclosed embodiments and techniques in each sectionto only that section.

Example Synthetic Data Generation

A three-dimensional (3D) model is based on mathematical representationsof surfaces of an object in three dimensions. Typically, surfaces of anobject in a 3D model are represented as vertices, curves, and/or voxels.One or more 3D models can be placed into a virtual setup, which issometimes referred to as a 3D scene. A typical 3D scene includes atleast a virtual camera, one or more virtual lights, and a scenebackground so that renderings of the 3D objects in the scene can begenerated. A 3D scene can also include parameters (e.g., cameraattributes, lighting attributes, and/or animation sequences) to allowdifferent renderings of the 3D model(s) to be created. It is noted thatthe use of the term camera and light in the context of the 3D modelrefers to capturing a 3D scene as if it were positioned under a lightsource and captured from the angle and position of a camera, though noreal cameras or light sources were used.

The disclosed embodiments rely at least in-part on 3D models that areplaced in a scene to facilitate the generation and selection of a properset of training data. In particular, 3D models of the human body can beobtained to generate synthetic training data for sign language training,which eliminates the need for manual search of suitable training images.For example, after a 3D model of a human body is obtained from one ofthe online sources, the model can be positioned in a 3D scene, eithermanually, using a motion capture suit, or using a 3D scanning system, toshow a gesture that represents a letter, a word, or a phrase in aparticular sign language. Using this setup, a reduced set of trainingimages can be obtained from rendered images of the 3D scene. As will bedescribed below, the set of images produced based on the above techniqueeliminates (or greatly reduces) the need for manual labeling and enablesoptimization of different aspects of the translation system.

FIG. 13 illustrates representative operations taken by a training systemimplemented in accordance with an example embodiment the disclosedtechnology. After obtaining a 3D model, the training system may pose orconfigure, at operation 1301, the 3D model (also known as rigging the 3Dmodel) to show a gesture that corresponds to a letter, a word, or aphrase in a sign language. The rigging process allows parts of the 3Dmodel that are relevant to the gesture to be marked as visible. In someimplementations, the 3D model can be displayed via a user interface ofthe training system on a display device, such as a computer screen or amonitor, and/or projected onto a projection screen.

A gesture can be static or animated. In some embodiments, the system canapply keyframes to the 3D model to show an animated sequence ofmovements. Here, a keyframe defines the starting and ending points of asmooth transition between the positions. For example, referring back tothe example of “apple” in the ASL, a 3D model can be rigged and/orkey-framed to show an animated sequence of movements with the right-handrotating around the knuckle.

Referring to FIG. 13, the training system can change, at operation 1302,a set of parameters of the 3D scene automatically to create differenttraining images. For example, the system can rotate, at operation 1303,the 3D model along one or more axes (e.g., X, Y, and/or Z axes) of thescene. The system can also zoom in and/or out, at operation 1304, toshow a bigger and/or smaller view of the 3D model. In some embodiments,the lighting parameters for the model can be changed at operation 1305.For example, the brightness of one or more lights can be changed to showa brighter or darker rendering of the model. The background of the 3Dscene can also be changed to mimic real-life scenarios. In someembodiments, the system can also change the color of the skin, theplacement of the facial features, and/or the textures of the clothing sothat the resulting training images have a large degree of variance. Insome embodiments, the system can set up a “fly-around” path, atoperation 1306, to produce scenes that are viewed from different angles(e.g., different “camera angles” that simulate movement of a camera withrespect to the object). The “fly-around” path allows the camera tocapture different views of the object without moving the object itself,thereby avoiding the risk of introducing undesired changes to therelative positions of the components in the model.

The system also generates, at 907, a set of two-dimensional (2D) imagesof the 3D scene as training images at predetermined intervals. The 2Dimages can be generated by rendering the 3D scene from the camera'sperspective. The rendering may include both photo-realistic renderingand real-time rendering. For example, techniques such as globalillumination can be used to generate photo-realistic renderings thatshow real-life lighting effects. Alternatively, renderings that do notrequire sophisticated lighting effects can be generated in real-timeusing a smaller amount of computational power. For static gestures, atraining image can be rendered after a change in one or more parametersof the 3D scene. In some embodiments, the system uses the “fly-around”path to generate renderings for different camera positions within apredetermined duration. The camera may have the same or differentorientations at different positions. An image can be generated each timethe camera updates its position long the path.

In some embodiments, the system imposes a set of displacements (e.g.,translations and/or rotations) to the model. An image can be renderedafter each translation and/or rotation of the model. For example, animage is rendered after the model is rotated around the Z axis for 36degrees, resulting in five images in total for a rotation of 180 degreesaround the Z axis. The “fly-around” path can also be used together withtranslations and/or rotations of the model to create a moresophisticated set of training images.

For animated gestures, images can be taken based on the length of theanimated sequence, as well as changes of the scene parameters. Forexample, for each change in a parameter value (e.g., the cameraposition), several images can be taken to capture the entire sequence ofmovements that represents “apple” in ASL.

To reduce the training data size while maintaining or even improving thetraining performance, the system can adopt a set of criteria to obtaindesirable parameter changes. In some embodiments, the system can firstset a particular value for the total number of images to be captured.The system then generates a “fly-around” path and/or displacements,based on the total number of images, to obtain a suitable amount ofvariance among the images. For example, the horizontal angles of view ofthe camera are within a range of 30 to 150 degrees. If the total numberof images to be captured is 30, the horizontal angles of view of thecamera can be equally distributed within the range (e.g., {34, 38, . . ., 150} degrees) so that the entire range of the angles of view isrepresented in the images.

In some embodiments, an iterative approach can be used. For example, thesystem first sets the total number of training images for a model to 30.The system then generates a “fly-around” path that includes fivedifferent camera locations in a predetermined time duration. For each ofthe camera locations, the system generates six rotations for the modelso that the model is rotated twice around each of the X, Y, and Z axes.After obtaining the 30 images, the training system is evaluated todetermine if it has been properly trained. For example, testing thatcovers various areas such as performance, accuracy, and/or number ofclasses/characteristics can be performed as a part of the evaluation. Ifthe training system determines that the trained translation system failsto meet one or more thresholds in one of the areas (e.g., the accuracyof the translation system is lower than a threshold), the trainingsystem can revise the “fly-around” path and/or displacements to generatea different set of training images. This process can repeat until thetranslation system is deemed as adequately trained.

In some embodiments, the system evaluates each of the training imagesbefore the full set is finalized. For example, after each image isgenerated, the system can feed the generated training image to thetranslation system. The recognition result of the translation system isevaluated to determine how many characteristics the system hasrecognized, the accuracy of the recognition, and/or the amount of timeused for performing the recognition. If any of these criteria (alsoshown in FIG. 12) deteriorates or shows no improvement for thetranslation system, the system can discard that training image, generateanother training image and repeat the process.

Example Automatic Labeling

To address the problem of manual labeling, the training system canautomatically label (e.g., operation 1308 in FIG. 13) one or morefeatures in the model by identifying polygons in the model thatrepresent the features of interest.

By the way of example and not by limitation, a feature can be asubsection of one or both hands, such as a finger or a palm, that canindicate a hand movement. In some embodiments, a feature can also beused to indicate non-manual activities. For example, facial featuresand/or body postures, such as the shape or a change in the shape of theleft eye, the right eye, the left shoulder, or the right cheek, tilt ofthe head, can be used to show a particular non-manual activity. Becausethe 3D model often represents a feature in the form of a group ofpolygons (e.g., a set of polygons can be grouped together and named as“right finger”), the feature to be labeled can be automaticallyhighlighted with proper bounding lines.

For example, FIG. 14A shows an example rendering of a monkey head andtwo hands. The two hands are identified as the feature of interest, sothe corresponding image shows highlighted bounding lines 1401 that formthe boundaries of the hands. FIG. 14B shows another example rendering ofthe monkey head and the two hands. In this example, the monkey head isidentified as the feature of interest, so the corresponding image showshighlighted bounding lines 1403 that form the boundary of the head. Itis thus evident that, as opposed to manual labeling process of imagesthat is either time consuming or can generate lots of noise (e.g., asdescribed in connection with FIG. 11), the disclosed labeling techniquetakes advantage of the already-known configuration of polygons thatform, e.g., the hand, a finger, the head, etc., to quickly andefficiently label the section(s) of interest, as will be furtherdescribed below.

Different groups of polygons in the 3D model may be organized indifferent ways. For example, a 3D model may include a first group ofpolygons to represent the fingers and a second group of polygons torepresent the palm. If the system wants to identify the feature “hand,”two groups of polygons can be selected at the same time. The polygonscan also be organized hierarchically. For example, as shown in FIG. 14C,a parent group “left hand” 1402, which includes the left fingers 1404and the left palm 1405, can be created to represent the feature “lefthand”. Similarly, another parent group “hands” 1401 can be created torepresent both features—“right hand” 1403 and “left hand” 1402—at thesame time. The “right hand” group can further include the right fingers1406 and the right palm 1407. To identify both hands, the system cansimply select a single group “hands” 1401. In some embodiments, thesystem may select the group “left hand” 1402 and another group lower inthe hierarchy such as “Fingers_right” 1406 for identifying a particulargesture. Regardless of how the polygons are organized, when using the 3Dmodels to generate synthetic training images, manual labeling is nolonger required because the selected polygons can provide the preciseboundaries of the features of interest.

Referring back to FIG. 13, the training images can then be exported, at1309, into a neural network engine of the translation system. In someembodiments, the neural network engine may include one or moreconvolutional neural networks (CNNs) and one or more recurrent neuralnetworks (RNNs), which may be combined in architectures that allowreal-time processing for of the training images. A convolutional neuralnetwork (CNN or ConvNet) is a class of deep, feedforward artificialneural networks that typically use a variation of multilayer perceptronsdesigned to require minimal preprocessing. A perceptron is a computermodel or computerized machine devised to represent or simulate theability of the brain to recognize and discriminate. This means that thenetwork learns the filters (normally through a training process) neededto identify the features of interest; filters that in traditionalalgorithms were hand-engineered. This independence from prior knowledgeand human effort in feature design is a major advantage of CNNs. CNNshave been successfully used for image (or more generally, visual)recognition and classification (e.g., identifying faces, objects andtraffic signs) by using the “convolution” operator to extract featuresfrom the input image. Convolution preserves the spatial relationshipbetween pixels by learning image features using input (morespecifically, training) data.

In contrast to the CNN, a recurrent neural network (RNN) is a type ofartificial neural network where connections between nodes form adirected graph along a sequence. This allows it to exhibit dynamictemporal behavior for a time sequence. Unlike feedforward neuralnetworks, RNNs can use their internal state to process sequences ofinputs. That is, RNNs have a feedback loop connected to their pastdecisions, which lets the RNN exhibit memory. For example, sequentialinformation is preserved in the recurrent network's hidden state, whichmanages to span many time steps as it cascades forward to affect theprocessing of each new example. It is finding correlations betweenevents separated by many moments, and these correlations are called“long-term dependencies”, because an event downstream in time dependsupon, and is a function of, one or more events that came before.

The neural network engine takes the training image(s) and performs thetraining accordingly, e.g., using the CNN(s) and/or RNN(s). In someembodiments, the neural network engine executes on one or more graphicsprocessing units to leverage the parallel computing power. As discussedabove, the training process can be iterative—by evaluating theperformance and/or accuracy of the neural network process, the trainingsystem can determine if re-generating a different set of training imagesis necessary.

FIG. 15A illustrates a set of operations that can be carried out by togenerate training images for a letter (e.g., the letter “a”) in the ASLin accordance with one or more embodiments of the disclosed technology.

Operation 1501: A 3D model of a human body is acquired. The 3D modelincludes selectable polygons for parts of the body.

Operation 1502: The training system selects one of the staticposes—e.g., the letter “a” in the ASL—as the gesture.

Operation 1503: The system includes tags that can be turned on or off tomap the model to the gesture. The parts of the model that are relevantto the sign of letter “a” are mapped (e.g., the polygons in these partsare marked as “true”).

Operation 1504: The system keeps the mapped parts visible and makes therest of the model invisible.

Operation 1505: A 3D scene is created to hold the visible parts to allowparameters changes by one or more scripts. The scripts control variousscene parameters, such as rotations, translations, camera angles,lighting, etc.

Operation 1506: The system identifies a feature of interest. After thefeature is identified, an outline of the visible parts of the feature(e.g., one or more bounding lines that form a precise boundary of thefeature) is automatically generated.

Operation 1507: The system sets criteria for taking one or more 2Dscreenshots. For example, the system determines that a total of 50images are needed as the training set. Other criteria, such as imageresolution or number of features to be labeled, can also be set.

Operation 1508: The system determines, based on the criteria, a“fly-around” path in a specified time duration. The system canoptionally determine additional parameters that can be changed in thetime duration.

Operation 1509: The scripts are executed to generate the desiredtraining images.

Operation 1510: The training images can be provided to a convolutionalneural network (e.g., TensorFlow) to perform training. In someembodiments, the training images are converted to other formats that arecompatible with the neural network process.

FIG. 15B depicts a high-level overview of a three-dimensional (3D) modelgeneration work flow in accordance with an example embodiment of thedisclosed technology.

Operation 1551: A 3D scene is created with a Humanoid model. Each partof the model, with descriptive tag names.

Operation 1553: The model is rigged to depict a sign language gesture.The system can generate animations based on any tagged part of a model,or across all tags of a model. Each part of the model can be interactedwith in all the ways the model's part is capable of, such as moving,rotating, and other similar kinds of movements. A first video clip canbe created and output to the file system.

Operation 1555: The first video clip is played back with a set ofsettings that includes at least the camera distance from the object, thecamera angle, the camera position, and the scene brightness.

Operation 1557: These settings are iterated over with each changedslightly, until all settings have been played through the fullanimation. For each iteration, a different video clip is generated.

Operation 1559: When the iterations are completed, a set of video clipsare generated with deliberate variations. The video clips are now usedto train the neural networks.

FIG. 16 shows a flowchart of an example method 1600 for training a signlanguage translation system in accordance with one or more embodimentsof the disclosed technology. The method 1600 includes, at 1602, adding athree-dimensional (3D) model into a 3D scene. The 3D model is positionedto show a gesture that represents a letter, a word, or a phrase in asign language. The method 1600 includes, at 1604, determining a set ofparameters of the 3D scene based on a predetermined number of trainingimages to be generated. The method 1600 includes, at 1606, generatingthe predetermined number of training images corresponding to the set ofparameters. Each image is generated based on at least one value of theset of parameters, and at least a subset of the parameters is adjustedsequentially in a time domain. The method 1600 also includes, at 1608,providing the predetermined number of training images to a neuralnetwork learning engine of the sign language translation system toperform training.

FIG. 17 shows a flowchart of an example method 1700 for providingtraining images for training a neural network of a sign languagetranslation system in accordance with one or more embodiments of thedisclosed technology. The method 1700 includes, at 1702, generating athree-dimensional (3D) scene that includes a 3D model representing atleast a part of a human body. The 3D model is positioned in the 3D sceneto simulate a gesture that represents a letter, a word, or a phrase in asign language. The method 1700 includes, at 1704, obtaining a valueindicative of a total number of training images to be generated. Themethod 1700 includes, at 1706, using the value indicative of the totalnumber of training images to determine a plurality of variations of the3D scene for generating of the training images. The method 1700includes, at 1708, applying each of plurality of variations to the 3Dscene to produce a plurality of modified 3D scenes. The method 1700 alsoincludes, at 1710, capturing an image of each of the plurality ofmodified 3D scenes to form the training images for a neural network ofthe sign language translation system.

FIG. 18 is a block diagram illustrating an example of the architecturefor a computer system or other control device 1800 that can be utilizedto implement various portions of the presently disclosed technology. InFIG. 18, the computer system 1800 includes one or more processors 1805and memory 1810 connected via an interconnect 1825. The interconnect1825 may represent any one or more separate physical buses, point topoint connections, or both, connected by appropriate bridges, adapters,or controllers. The interconnect 1825, therefore, may include, forexample, a system bus, a Peripheral Component Interconnect (PCI) bus, aHyperTransport or industry standard architecture (ISA) bus, a smallcomputer system interface (SCSI) bus, a universal serial bus (USB), IIC(I2C) bus, or an Institute of Electrical and Electronics Engineers(IEEE) standard 674 bus, sometimes referred to as “Firewire.”

The processor(s) 1805 may include central processing units (CPUs) tocontrol the overall operation of, for example, the host computer. Theprocessor(s) 1805 can also include one or more graphics processing units(GPUs). In certain embodiments, the processor(s) 1805 accomplish this byexecuting software or firmware stored in memory 1810. The processor(s)1805 may be, or may include, one or more programmable general-purpose orspecial-purpose microprocessors, digital signal processors (DSPs),programmable controllers, application specific integrated circuits(ASICs), programmable logic devices (PLDs), or the like, or acombination of such devices.

The memory 1810 can be or include the main memory of the computersystem. The memory 1810 represents any suitable form of random accessmemory (RAM), read-only memory (ROM), flash memory, or the like, or acombination of such devices. In use, the memory 1810 may contain, amongother things, a set of machine instructions which, upon execution byprocessor 1805, causes the processor 1805 to perform operations toimplement embodiments of the presently disclosed technology.

Also connected to the processor(s) 1805 through the interconnect 1825 isa (optional) network adapter 1815. The network adapter 1815 provides thecomputer system 1800 with the ability to communicate with remotedevices, such as the storage clients, and/or other storage servers, andmay be, for example, an Ethernet adapter or Fiber Channel adapter.

Based on empirical data obtained using the disclosed techniques, it hasbeen determined that a small amount of training images (e.g., around 50images) is sufficient to train a pattern and gesture recognition systemeffectively. Thus, the number of training images can be greatly reduced.As the size of training data (e.g., the number of training images)becomes smaller, the performance of the training process is increasedaccordingly. For example, the reduction in processing can enable theimplementation of the disclosed translation system using fewer hardware,software and/or power resources, such as implementation on a handhelddevice. Additionally, or alternatively, the gained computational cyclescan be traded off to improve other aspects of the system. For example,in some implementations, a small number of training images allows thesystem to select more features in the 3D model. Thus, the trainingaspect can be improved due to the system's ability to recognize a largernumber of classes/characteristics per training data set. Furthermore,because the features are labeled automatically with their preciseboundaries (without introducing noise pixels), the accuracy of thetraining is also improved.

It is thus evident that the disclosed techniques can be implemented invarious embodiments to optimize one or more aspects (e.g., performance,the number of classes/characteristics, accuracy) of the training processof an AI system that uses neural networks, such as a sign languagetranslation system. It is further noted that while the provided examplesfocus on recognizing and translating sign languages, the disclosedtechniques are not limited in the field of sign language translation andcan be applied in other areas that require pattern and/or recognition.For example, the disclosed techniques can be used in various embodimentsto train a pattern and gesture recognition system that includes a neuralnetwork learning engine.

In one example aspect, an apparatus for training a sign languagetranslation system is disclosed. The apparatus includes a processor anda memory including processor executable code. The processor executablecode, upon execution by the processor, causes the processor to generatea three-dimensional (3D) scene that includes a 3D model representing atleast a part of a human body. The 3D model is positioned in the 3D sceneto simulate a gesture that represents a letter, a word, or a phrase in asign language. The processor executable code upon execution by theprocessor configures the processor to obtain a value indicative of atotal number of training images to be generated, use the valueindicative of the total number of training images to determine aplurality of variations of the 3D scene for generating of the trainingimages, apply each of plurality of variations to the 3D scene to producea plurality of modified 3D scenes, and capture an image of each of theplurality of modified 3D scenes to form the training images for a neuralnetwork of the sign language translation system.

In some embodiments, the processor executable code, upon execution bythe processor, further configures the processor to, for each of thetraining images, automatically generate a label that corresponds to afeature of interest, the label comprising one or more bounding linesthat delineates a precise boundary of the feature of interest. In someembodiments, the precise boundary of the feature of interest isgenerated based on a group of polygons that collectively form thefeature of interest in the 3D model. In some embodiments, the feature ofinterest has an irregularly shaped boundary. In some embodiments, thefeature of interest is associated with a hand movement. In someembodiments, the feature is associated with a non-manual activity.

In some embodiments, the processor executable code, upon execution bythe processor, configures the processor to determine the plurality ofvariations of the 3D scene based on a set of parameters that specify atleast one of: a position of the 3D model, an angle of 3D model, aposition of a camera, an orientation of a camera, a lighting attribute,a texture of a subsection of the 3D model, or a background of the 3Dscene. In some embodiments, the processor executable code, uponexecution by the processor, configures the processor to apply each ofplurality of variations to the 3D scene by changing the 3D scene in atemporal sequence in accordance with the set of parameters.

In some embodiments, the processor executable code, upon execution bythe processor, further configures the processor to obtain an evaluationof the sign language translation system after the sign languagetranslation system performs training and re-generate another set oftraining images upon a determination that the sign language translationsystem fails to meet one or more predetermined criteria. In someembodiments, the one or more predetermined criteria includes at leastone of: a performance of the neural network, an accuracy of the neuralnetwork, or a number of characteristics that the neural network iscapable of recognizing.

In some embodiments, the value indicative of a total number of trainingimages is less than or equal to 50. In some embodiments, the processorexecutable code, upon execution by the processor, further configures theprocessor to obtain an evaluation of the sign language translationsystem for each of the training images one at a time and, for at leastone of the training images, upon a determination that the sign languagetranslation system performance in identifying a feature interest usingthe at least one of the training images has failed to improve from itsperformance based on a previous training image, discard the at least onetraining image.

In another example aspect, a method for providing training images fortraining a neural network of a sign language translation system isdisclosed. The method includes generating a three-dimensional (3D) scenethat includes a 3D model representing at least a part of a human body.The 3D model is positioned in the 3D scene to simulate a gesture thatrepresents a letter, a word, or a phrase in a sign language. The methodincludes obtaining a value indicative of a total number of trainingimages to be generated. The method includes using the value indicativeof the total number of training images to determine a plurality ofvariations of the 3D scene for generating of the training images. Themethod includes applying each of plurality of variations to the 3D sceneto produce a plurality of modified 3D scenes. The method also includescapturing an image of each of the plurality of modified 3D scenes toform the training images for a neural network of the sign languagetranslation system.

In some embodiments, the method further includes, for each of thetraining images, automatically generating a label that corresponds to afeature of interest, the label comprising one or more bounding linesthat delineates a precise boundary of the feature of interest. In someembodiments, the precise boundary of the feature of interest isgenerated based on a group of polygons that collectively form thefeature of interest in the 3D model. In some embodiments, the feature ofinterest has an irregularly shaped boundary. In some embodiments, thefeature of interest is associated with a hand movement. In someembodiments, the feature is associated with a non-manual activity.

In some embodiments, determining the plurality of variations of the 3Dscene is based on a set of parameters that specify at least one of: aposition of the 3D model, an angle of 3D model, a position of a camera,an orientation of a camera, a lighting attribute, a texture of asubsection of the 3D model, or a background of the 3D scene. In someembodiments, applying each of plurality of variations to the 3D sceneincludes changing the 3D scene in a temporal sequence in accordance withthe set of parameters.

In some embodiments, the method further includes obtaining an evaluationof the sign language translation system after the sign languagetranslation system performs training and re-generating another set oftraining images upon a determination that the sign language translationsystem fails to meet one or more predetermined criteria. In someembodiments, the one or more predetermined criteria includes at leastone of: a performance of the neural network, an accuracy of the neuralnetwork, or a number of characteristics that the neural network iscapable of recognizing.

In some embodiments, the value indicative of a total number of trainingimages is less than or equal to 50. In some embodiments, the methodfurther includes obtaining an evaluation of the sign languagetranslation system for each of the training images one at a time and,for at least one of the training images, upon a determination that thesign language translation system performance in identifying a featureinterest using the at least one of the training images has failed toimprove from its performance based on a previous training image,discarding the at least one training image.

In another example aspect, a non-transitory computer readable mediumhaving code stored thereon is disclosed. The code, upon execution by aprocessor, causes the processor to implement a method that includesgenerating a three-dimensional (3D) scene that includes a 3D modelrepresenting at least a part of a human body. The 3D model is positionedin the 3D scene to simulate a gesture that represents a letter, a word,or a phrase in a sign language. The method also includes obtaining avalue indicative of a total number of training images to be generated,using the value indicative of the total number of training images todetermine a plurality of variations of the 3D scene for generating ofthe training images, applying each of plurality of variations to the 3Dscene to produce a plurality of modified 3D scenes, and capturing animage of each of the plurality of modified 3D scenes to form thetraining images for a neural network of the sign language translationsystem.

In some embodiments, the method further comprises, for each of thetraining images, automatically generating a label that corresponds to afeature of interest, the label comprising one or more bounding linesthat delineates a precise boundary of the feature of interest. In someembodiments, the precise boundary of the feature of interest isgenerated based on a group of polygons that collectively form thefeature of interest in the 3D model. In some embodiments, the feature ofinterest has an irregularly shaped boundary. In some embodiments, thefeature of interest is associated with a hand movement. In someembodiments, the feature is associated with a non-manual activity.

In some embodiments, determining the plurality of variations of the 3Dscene is based on a set of parameters that specify at least one of: aposition of the 3D model, an angle of 3D model, a position of a camera,an orientation of a camera, a lighting attribute, a texture of asubsection of the 3D model, or a background of the 3D scene. In someembodiments, applying each of plurality of variations to the 3D sceneincludes changing the 3D scene in a temporal sequence in accordance withthe set of parameters.

In some embodiments, the method further includes obtaining an evaluationof the sign language translation system after the sign languagetranslation system performs training and re-generating another set oftraining images upon a determination that the sign language translationsystem fails to meet one or more predetermined criteria. In someembodiments, the one or more predetermined criteria includes at leastone of: a performance of the neural network, an accuracy of the neuralnetwork, or a number of characteristics that the neural network iscapable of recognizing.

In some embodiments, the value indicative of a total number of trainingimages is less than or equal to 50. In some embodiments, the methodfurther includes obtaining an evaluation of the sign languagetranslation system for each of the training images one at a time and,for at least one of the training images, upon a determination that thesign language translation system performance in identifying a featureinterest using the at least one of the training images has failed toimprove from its performance based on a previous training image,discarding the at least one training image.

In another example aspect, an apparatus for training a patternrecognition system having a neural network engine is disclosed. Theapparatus includes one or more processors and a memory includingprocessor executable code. The processor executable code, upon executionby the one or more processors, causes the one or more processors togenerate a three-dimensional (3D) scene that includes a 3D modelrepresenting an object. The 3D model comprising a plurality of polygonalsubsections that collectively form the object. The processor executablecode, upon execution by the one or more processors, also causes the oneor more processors to determine a total number of training images to begenerated for training the neural network, determine, based on the totalnumber of training images, a plurality of parameter variations andapplying each of plurality of the parameter variations to the 3D sceneto produce a plurality of modified 3D scenes. The modified 3D scenesinclude at least one set of variations to a spatial position of themoving object in accordance with a temporal sequence. The processorexecutable code, upon execution by the one or more processors, alsocauses the one or more processors to capture an image of each of theplurality of modified 3D scenes to form the training images for theneural network learning engine, and, for each of the training images,automatically generate a label that corresponds to a feature of interestof the 3D model. The label includes one or more bounding lines thatdelineates a precise boundary of the feature of interest by combining aninteger number of polygonal subsections of the 3D model.

In another example aspect, a method for training a sign languagetranslation system is disclosed. The method includes adding athree-dimensional (3D) model into a 3D scene. The 3D model is positionedto show a gesture that represents a letter, a word, or a phrase in asign language. The method includes determining a set of parameters ofthe 3D scene based on a predetermined number of training images to begenerated and generating the predetermined number of training imagescorresponding to the set of parameters. Each image is generated based onat least one value of the set of parameters, and at least a subset ofthe parameters is adjusted sequentially in a time domain. The methodalso includes providing the predetermined number of training images to aneural network learning engine of the sign language translation systemto perform training. In some embodiments, the predetermined number isless than or equal to 50.

In some embodiments, the method further includes obtaining an evaluationof the sign language translation system after the sign languagetranslation system performs training; and re-generating a second set oftraining images when the evaluation indicates that the sign languagetranslation system fails to meet one or more predetermined criteria. Insome embodiments, the set of parameters of the 3D scene includes atleast one of: a position of the 3D model, an angle of 3D model, aposition of a camera, an orientation of the camera, a lightingattribute, a texture of a subsection of the 3D model, or a background ofthe 3D scene.

In some embodiments, the method further includes identifying a featurewithin the 3D model, wherein one or more of the predetermined number oftraining images include a label that corresponds to the feature. In someembodiments, the feature indicates a hand movement of the gesture. Insome embodiments, the feature indicates a non-manual activity of thegesture. In some embodiments, the label includes one or more boundinglines that form a precise boundary of the feature. In some embodiments,the one or more bounding lines are automatically selected when thefeature is identified.

In another example aspect, an apparatus for training a sign languagetranslation system is disclosed. The apparatus includes a processor anda memory including processor executable code. The processor executablecode, upon execution by the processor, causes the processor to add athree-dimensional (3D) model into a 3D scene. The 3D model is positionedto show a gesture that represents a letter, a word, or a phrase in asign language. The processor executable code upon execution by theprocessor configures the processor to determine a set of parameters ofthe 3D scene based on a predetermined number of training images to begenerated and generate the predetermined number of training imagescorresponding to the set of parameters. Each image is generated based onat least one value of the set of parameters, and at least a subset ofthe parameters is adjusted sequentially in a time domain. The processorexecutable code, upon execution by the processor, also configures theprocessor to provide the predetermined number of training images to aneural network learning engine of the sign language translation systemto perform training. In some embodiments, the predetermined number isless than or equal to 50.

In some embodiments, the processor executable code, upon execution bythe processor, configures the processor to obtain an evaluation of thesign language translation system after the sign language translationsystem performs training and re-generate a second set of training imageswhen the evaluation indicates that the sign language translation systemfails to meet one or more predetermined criteria. In some embodiments,the set of parameters of the 3D scene includes at least one of: aposition of the 3D model, an angle of 3D model, a position of a camera,an orientation of the camera, a lighting attribute, a texture of asubsection of the 3D model, or a background of the 3D scene.

In some embodiments, the processor executable code, upon execution bythe processor, further configures the processor to identify a featurewithin the 3D model, wherein one or more of the predetermined number oftraining images include a label that corresponds to the feature. In someembodiments, the feature indicates a hand movement of the gesture. Insome embodiments, the feature indicates a non-manual activity of thegesture. In some embodiments, the label includes one or more boundinglines that form a precise boundary of the feature. In some embodiments,the one or more bounding lines are automatically selected when thefeature is identified.

In yet another example aspect, a method for producing an imagerecognition system having a neural network engine is disclosed. Themethod includes adding a three-dimensional (3D) model into a 3D scene,determining a set of parameters of the 3D scene based on a predeterminednumber of training images to be generated, identifying a feature withinthe 3D model, and generating the predetermined number of training imagescorresponding to the set of parameters. Each image is generated based onat least one value of the set of parameters, and at least a subset ofthe parameters is adjusted sequentially in a time domain. One or more ofthe predetermined number of training images include a label thatcorresponds to the feature. The label is automatically selected based onone or more bounding lines that form a precise boundary of the feature.The predetermined number of training images forms a set of trainingimages for the neural network learning engine of the image recognitionsystem.

Implementations of the subject matter and the functional operationsdescribed in this patent document can be implemented in various systems,digital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.Implementations of the subject matter described in this specificationcan be implemented as one or more computer program products, e.g., oneor more modules of computer program instructions encoded on a tangibleand non-transitory computer readable medium for execution by, or tocontrol the operation of, data processing apparatus. The computerreadable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The term “data processing unit” or “dataprocessing apparatus” encompasses all apparatus, devices, and machinesfor processing data, including by way of example a programmableprocessor, a computer, or multiple processors or computers. Theapparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Computer readable media suitable for storingcomputer program instructions and data include all forms of nonvolatilememory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

It is intended that the specification, together with the drawings, beconsidered exemplary only, where exemplary means an example.

While this patent document contains many specifics, these should not beconstrued as limitations on the scope of any invention or of what may beclaimed, but rather as descriptions of features that may be specific toparticular embodiments of particular inventions. Certain features thatare described in this patent document in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in this patent document should not be understoodas requiring such separation in all embodiments.

Only a few implementations and examples are described and otherimplementations, enhancements and variations can be made based on whatis described and illustrated in this patent document.

What is claimed is:
 1. An apparatus for training a machine learningsystem, comprising: a processor, and a memory including processorexecutable code, wherein the processor executable code upon execution bythe processor causes the processor to: generate a three-dimensional (3D)scene that includes a 3D model representing a target object, wherein the3D model is positioned in the 3D scene to simulate a gesture; determinea plurality of variations of the 3D scene for generating a number oftraining images; apply each of the plurality of variations to the 3Dscene to produce a plurality of modified 3D scenes; capture an image ofeach of the plurality of modified 3D scenes to form the training imagesfor a neural network of the machine learning system; and automaticallygenerate, for each of the training images, a label that corresponds to afeature of interest associated with the gesture, the label comprisingone or more bounding lines that delineates a precise boundary of thefeature of interest, wherein the precise boundary of the feature ofinterest is generated based on a group of polygons that form the featureof interest in the 3D model.
 2. The apparatus of claim 1, wherein thenumber of training images to be generated is predetermined.
 3. Theapparatus of claim 1, wherein the number of training images is less thanor equal to
 50. 4. The apparatus of claim 1, wherein the feature ofinterest has an irregularly shaped boundary.
 5. The apparatus of claim1, wherein the processor executable code upon execution by the processorconfigures the processor to determine the plurality of variations of the3D scene based on a set of parameters that specifies at least one of: aposition of the 3D model, an angle of the 3D model, a position of acamera, an orientation of a camera, a lighting attribute, a texture of asubsection of the 3D model, or a background of the 3D scene.
 6. Theapparatus of claim 1, wherein the processor executable code uponexecution by the processor configures the processor to apply each of theplurality of variations to the 3D scene by changing the 3D scene in atemporal sequence.
 7. The apparatus of claim 1, wherein the processorexecutable code upon execution by the processor configures the processorto: evaluate the machine learning system after the machine learningsystem performs a training operation based on the training images; andre-generate another set of training images upon a determination that themachine learning system fails to meet one or more predeterminedcriteria.
 8. The apparatus of claim 7, wherein the one or morepredetermined criteria includes at least one of: a performance of theneural network, an accuracy of the neural network, or a number ofcharacteristics that the neural network is capable of recognizing. 9.The apparatus of claim 1, wherein the processor executable code uponexecution by the processor causes the processor to: evaluate the machinelearning system for each of the training images one at a time; and forat least one of the training images, upon a determination that themachine learning system performance in identifying a feature of interestusing the at least one of the training images has failed to improve fromits performance based on a previous training image, discard the at leastone of the training images.
 10. A method for providing training imagesfor training a neural network of a machine learning system, comprising:generating a three-dimensional (3D) scene that includes a 3D modelrepresenting a target object, wherein the 3D model is positioned in the3D scene to simulate a gesture; determine a plurality of variations ofthe 3D scene for generating a number of training images; applying eachof the plurality of variations to the 3D scene to produce a plurality ofmodified 3D scenes; capturing an image of each of the plurality ofmodified 3D scenes to form the training images for the neural network ofthe machine learning system; and automatically generating, for each ofthe training images, a label that corresponds to a feature of interestassociated with the gesture, the label comprising one or more boundinglines that delineates a precise boundary of the feature of interest,wherein the precise boundary of the feature of interest is generatedbased on a group of polygons that form the feature of interest in the 3Dmodel.
 11. The method of claim 10, wherein the number of training imagesto be generated is predetermined.
 12. The method of claim 10, whereinthe number of training images is less than or equal to
 50. 13. Themethod of claim 10, wherein the feature of interest has an irregularlyshaped boundary.
 14. The method of claim 10, wherein determining theplurality of variations of the 3D scene is based on a set of parametersthat specifies at least one of: a position of the 3D model, an angle ofthe 3D model, a position of a camera, an orientation of a camera, alighting attribute, a texture of a subsection of the 3D model, or abackground of the 3D scene.
 15. The method of claim 10, wherein applyingeach of the plurality of variations to the 3D scene comprises changingthe 3D scene in a temporal sequence.
 16. The method of claim 10, furthercomprising: evaluating the machine learning system after the machinelearning system performs training; and re-generating another set oftraining images upon a determination that the machine learning systemfails to meet one or more predetermined criteria.
 17. The method ofclaim 16, wherein the one or more predetermined criteria includes atleast one of: a performance of the neural network, an accuracy of theneural network, or a number of characteristics that the neural networkis capable of recognizing.
 18. The method of claim 10, furthercomprising: evaluating the machine learning system for each of thetraining images one at a time; and for at least one of the trainingimages, upon a determination that the machine learning systemperformance in identifying a feature interest using the at least one ofthe training images has failed to improve from its performance based ona previous training image, discarding the at least one training image.19. A non-transitory computer readable medium having code storedthereon, the code upon execution by a processor, causing the processorto implement a method that comprises: generating a three-dimensional(3D) scene that includes a 3D model representing a target object,wherein the 3D model is positioned in the 3D scene to simulate agesture; determining a plurality of variations of the 3D scene forgenerating a number of training images; applying each of the pluralityof variations to the 3D scene to produce a plurality of modified 3Dscenes; capturing an image of each of the plurality of modified 3Dscenes to form the training images for a neural network of the machinelearning system; and automatically generating, for each of the trainingimages, a label that corresponds to a feature of interest associatedwith the gesture, the label comprising one or more bounding lines thatdelineates a precise boundary of the feature of interest, wherein theprecise boundary of the feature of interest is generated based on agroup of polygons that form the feature of interest in the 3D model. 20.The non-transitory computer readable medium of claim 19, wherein thenumber of training images to be generated is predetermined.
 21. Thenon-transitory computer readable medium of claim 19, wherein the numberof training images is less than or equal to
 50. 22. The non-transitorycomputer readable medium of claim 19, wherein the feature of interesthas an irregularly shaped boundary.
 23. The non-transitory computerreadable medium of claim 19, wherein determining the plurality ofvariations of the 3D scene is based on a set of parameters thatspecifies at least one of: a position of the 3D model, an angle of the3D model, a position of a camera, an orientation of a camera, a lightingattribute, a texture of a subsection of the 3D model, or a background ofthe 3D scene.
 24. The non-transitory computer readable medium of claim23, wherein applying each of the plurality of variations to the 3D sceneincludes changing the 3D scene in a temporal sequence in accordance withthe set of parameters.
 25. The non-transitory computer readable mediumof claim 19, wherein the method further comprises: obtaining anevaluation of the machine learning system after the machine learningsystem performs training; and re-generating another set of trainingimages upon a determination that the machine learning system fails tomeet one or more predetermined criteria.
 26. The non-transitory computerreadable medium of claim 25, wherein the one or more predeterminedcriteria includes at least one of: a performance of the neural network,an accuracy of the neural network, or a number of characteristics thatthe neural network is capable of recognizing.
 27. The non-transitorycomputer readable medium of claim 19, wherein the method furthercomprises: obtaining an evaluation of the machine learning system foreach of the training images one at a time; and for at least one of thetraining images, upon a determination that the machine learning systemperformance in identifying a feature interest using the at least one ofthe training images has failed to improve from its performance based ona previous training image, discarding the at least one training image.